Since the late 1990s, there has been a burst of research on robotic devices for poststroke rehabilitation. Robot-mediated therapy produced improvements on recovery of motor capacity; however, so far, the use of robots has not shown qualitative benefit over classical therapist-led training sessions, performed on the same quantity of movements. Multidegree-of-freedom robots, like the modern upper-limb exoskeletons, enable a distributed interaction on the whole assisted limb and can exploit a large amount of sensory feedback data, potentially providing new capabilities within standard rehabilitation sessions. Surprisingly, most publications in the field of exoskeletons focused only on mechatronic design of the devices, while little details were given to the control aspects. On the contrary, we believe a paramount aspect for robots potentiality lies on the control side. Therefore, the aim of this review is to provide a taxonomy of currently available control strategies for exoskeletons for neurorehabilitation, in order to formulate appropriate questions toward the development of innovative and improved control strategies.

When developing robotic exoskeletons, the design of physical connections between the device and the human limb it is connected to is a crucial problem. Indeed, using an embedment at each connection point leads to uncontrollable forces at the interaction port, induced by hyperstaticity. In practice, these forces may be large because in general the human limb kinematics and the exoskeleton kinematics differ. To cope with hyperstaticity, literature suggests the addition of passive mechanisms inside the mechanism loops. However, empirical solutions proposed so far lack proper analysis and generality. In this paper, we study the general problem of connecting two similar kinematic chains through multiple passive mechanisms. We derive a constructive method that allows the determination of all the possible distributions of freed Degrees of Freedom (DoFs) across the different fixation mechanisms. It also provides formal proofs of global isostaticity. Practical usefulness is illustrated through two examples with conclusive experimental results: a preliminary study made on a manikin with an arm exoskeleton controlling the movement (passive mode) and a larger campaign on ten healthy subjects performing pointing tasks with a transparent robot (active mode).

While motor interaction between a robot and a human, or between humans, has important implications for society as well as promising applications, little research has been devoted to its investigation. In particular, it is important to understand the different ways two agents can interact and generate suitable interactive behaviors. Towards this end, this paper introduces a framework for the description and implementation of interactive behaviors of two agents performing a joint motor task. A taxonomy of interactive behaviors is introduced, which can classify tasks and cost functions that represent the way each agent interacts. The role of an agent interacting during a motor task can be directly explained from the cost function this agent is minimizing and the task constraints. The novel framework is used to interpret and classify previous works on human-robot motor interaction. Its implementation power is demonstrated by simulating representative interactions of two humans. It also enables us to interpret and explain the role distribution and switching between roles when performing joint motor tasks.

Upper-limb impairment after stroke is caused by weakness, loss of individual joint control, spasticity, and abnormal synergies. Upper-limb movement frequently involves abnormal, stereotyped, and fixed synergies, likely related to the increased use of sub-cortical networks following the stroke. The flexible coordination of the shoulder and elbow joints is also disrupted. New methods for motor learning, based on the stimulation of activity-dependent neural plasticity have been developed. These include robots that can adaptively assist active movements and generate many movement repetitions. However, most of these robots only control the movement of the hand in space. The aim of the present text is to analyze the potential of robotic exoskeletons to specifically rehabilitate joint motion and particularly inter-joint coordination. First, a review of studies on upper-limb coordination in stroke patients is presented and the potential for recovery of coordination is examined. Second, issues relating to the mechanical design of exoskeletons and the transmission of constraints between the robotic and human limbs are discussed. The third section considers the development of different methods to control exoskeletons: existing rehabilitation devices and approaches to the control and rehabilitation of joint coordinations are then reviewed, along with preliminary clinical results available. Finally, perspectives and future strategies for the design of control mechanisms for rehabilitation exoskeletons are discussed.

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While a large number of robotic exoskeletons have been designed by research teams for rehabilitation, it remains rather difficult to analyse their ability to finely interact with a human limb: no performance indicators or general methodology to characterize this capacity really exist. This is particularly regretful at a time when robotics are becoming a recognized rehabilitation method and when complex problems such as 3-D movement rehabilitation and joint rotation coordination are being addressed. The aim of this paper is to propose a general methodology to evaluate, through a reduced set of simple indicators, the ability of an exoskeleton to interact finely and in a controlled way with a human. The method involves measurement and recording of positions and forces during 3-D point to point tasks. It is applied to a 4 degrees-of-freedom limb exoskeleton by way of example.

Moving smoothly is generally considered as a higher-order goal of motor control and moving jerkily as a witness of clumsiness or pathology, yet many common and well-controlled movements (e.g., tracking movements) have irregular velocity profiles with widespread fluctuations. The origin and nature of these fluctuations have been associated with the operation of an intermittent process but in fact remain poorly understood. Here we studied velocity fluctuations during slow movements, using combined experimental and theoretical tools. We recorded arm movement trajectories in a group of healthy participants performing back-and-forth movements at different speeds, and we analyzed velocity profiles in terms of series of segments (portions of velocity between 2 minima). We found that most of the segments were smooth (i.e., corresponding to a biphasic acceleration) and had constant duration irrespective of movement speed and linearly increasing amplitude with movement speed. We accounted for these observations with an optimal feedback control model driven by a staircase goal position signal in the presence of sensory noise. Our study suggests that one and the same control process can explain the production of fast and slow movements, i.e., fast movements emerge from the immediate tracking of a global goal position and slow movements from the successive tracking of intermittently updated intermediate goal positions. NEW & NOTEWORTHY We show in experiments and modeling that slow movements could result from the brain tracking a sequence of via points regularly distributed in time and space. Accordingly, slow movements would differ from fast movement by the nature of the guidance and not by the nature of control. This result could help in understanding the origin and nature of slow and segmented movements frequently observed in brain disorders.

This paper summarizes findings on the growing field of role assignment policies for human-robot motor interaction. This topic has been investigated by researchers in the psychological theory of joint action, in human intention detection, force control, human-human physical interaction, as well as roboticists interested in developing robots with capabilities for efficient motor interaction with humans. Our goal is to promote fruitful interaction between these distinct communities by: (i) examining the role assignment policies for human-robot joint motor action in experimental psychology and robotics studies; and (ii) informing researchers in human-human interaction on existing work in the robotic field. After an overview of roles assignment in current robotic assistants, this paper examines key results about shared control between a robot and a human performing interactive motor tasks. Research on motor interaction between two humans has inspired recent developments that may extend the use of robots to applications requiring continuous mechanical interaction with humans.